Research shows fear, uncertainty and doubt surrounding AI adoption is giving way to excitement, optimism and proven outcomes for organizations
While both consumers and enterprises want to use Artificial Intelligence (AI) to a greater extent – and many executives have put it as a top strategic priority for 2021 – challenges still remain that have hindered actual adoption, according to Juniper Networks’ study. Juniper conducted the survey of 700 IT decision makers with direct involvement in their organization’s AI and/or machine learning plans or actual deployments to assesses the attitudes, perceptions and concerns of the technology.
In the corporate world, AI is just starting to be utilized to automate daily tasks, such as chatbots for customer service, bank reconciliations and smart workflows for IT trouble ticket management. In fact, according to Juniper’s research, 95% of all respondents believe their organization would benefit from embedding AI into their daily operations, products and services. However, only 6% of C-level leaders (163 total surveyed in study) reported adoption of AI-powered solutions across their organization today.
Juniper’s research shows that this gap lies within the following three challenges, ranked by respondents as the top inhibitors to adoption:
- AI-Ready Technology Stacks: Respondents ranked developing AI models and data sets that can be used across the company as the top technology-related challenge. There is a need for investment in robust cloud solutions and preparation of the right data for AI to use, as more than half of executives reported that their company is likely to collect telemetry data to enhance AI to improve user experience, as well as ensure sensitive data is protected in the process.
- Workforce Readiness: 73% of respondents’ organizations are struggling with preparing and expanding their workforce to integrate with AI systems. Meanwhile, C-level respondents reported they feel it’s more of a priority to hire people to develop AI capabilities within an organization (Priority No. 1) than it is to train end users to operate the tools themselves (Priority No. 3).
- AI Governance: 67% of respondents reported that AI has been identified as a priority by their organization’s leadership team for their FY21 strategic plan and 84% of executives agree cross-functional executive sponsorship and involvement is critical for AI to integrate into their products and services. However, only 7% of executives reported that they have not identified a company-wide AI leader who oversees AI strategy and governance.
Although AI does come with its sets of challenges, our research shows that organizations that have already adopted and harnessed AI are showing real and meaningful outcomes, providing optimism and excitement. The survey revealed IT and Operations are the most common business areas organizations are currently utilizing AI, where positive changes like operational efficiencies and enhanced user experience are being seen. The research also shows that as organizations scale their AI capabilities and integrate their employees into their solutions, user satisfaction steadily rose and the time given back allows employees to focus on value-add tasks they could not previously accomplish.
“As a CIO, when I see so much interest in an emerging technology, I always need to remind people there are pitfalls if it’s not managed correctly. For AI, there is no doubt that there is light at the end of the challenge-filled tunnel, and significant potential to generate even more meaningful and incredible outcomes than we’ve seen so far. By focusing on upskilling their workforce, investing in strong infrastructure – including data, cloud and networking capabilities – and implementing enterprise-wide AI governance, organizations are preparing for the digital workforce of tomorrow,” comments Sharon Mandell, SVP and CIO, Juniper Network.
Mandell adds, “At Juniper, our mission is to leverage AI to simplify operations and deliver superior experiences for enterprisers, service providers and cloud providers. Automated network monitoring, management and troubleshooting from client-to-cloud provides better insight so that operators can focus on higher level tasks and end users can focus on consuming value – without the need to hire outside people with AI expertise.”